Comparison of M Estimation, S Estimation, with MM Estimation to Get the Best Estimation of Robust Regression in Criminal Cases in Indonesia
Perbandingan Estimasi M, Estimasi S, dengan Estimasi MM
DOI:
https://doi.org/10.20956/j.v18i2.18630Keywords:
Regression analysis, Robust regression, M Estimation, S Estimation, MM Estimation.Abstract
Crime incidents that occurred in Indonesia in 2019 based on Survey Based Data on criminal data sourced from the National Socio-Economic Survey and Village Potential Data Collection produced by the Central Statistics Agency recorded 269,324 cases. The high crime rate is caused by several factors, including poverty and population density. Determination of the most influential factors in criminal acts in Indonesia can be done with Regression Analysis. One method of Regression Analysis that is very commonly used is the Least Square Method. However, Regression Analysis can be used if the assumption test is met. If outliers are found, then the assumption test is not completed. The outlier problem can be overcome by using a robust estimation method. This study aims to determine the best estimation method between Maximum Likelihood Type (M) estimation, Scale (S) estimation, and Method of Moment (MM) estimation on Robust Regression. The best estimate of Robust Regression is the smallest Residual Standard Error (RSE) value and the largest Adjusted R-square. The analysis of case studies of criminal acts in Indonesia in 2019 showed that the best estimate was the S estimate with an RSE value of 4226 and an Adjusted R-square of 0.98
References
Algifari. 2000. Analisis regresi: teori, kasus, dan solusi. BPFE UGM.
Andriani, D. P. 2014. Regresi linier berganda. Brawijaya University, 1–45.
Andriani, S. 2017. Uji Park dan uji Breusch Pagan Godfrey dalam pendeteksian Heteroskedastisitas pada analisis Regresi. Al-Jabar: Jurnal Pendidikan Matematika, Vol 8, No.1, 63–72.
BPS-Statistics Indonesia. 2020. Criminal statistics 2020. BPS-Statistics Indonesia.
Briliant, E. H., & Kurniawan, M. H. S. (2019). Perbandingan Regresi Linier Berganda dan Regresi Buckley- James pada analisis survival data Tersensor Kanan. Proceedings of The 1st STEEEM 2019, Vol 1, No.1, 1–19.
Chen, C. 2002. Statistics and Data Robust Regression and Outlier Detection with the ROBUSTREG Procedure. In Statistics and Data Analysis (Issue September).
Ghozali, I. 2009. Aplikasi analisis Multivariate dengan Program SPSS. Badan Penerbit UNDIP.
Mardiatmoko, G. 2020. Pentingnya uji Asumsi Klasik pada analisis Regresi Linier Berganda. BAREKENG: Jurnal Ilmu Matematika Dan Terapan, Vol 14, No.3, 333–342.
Maulana, A. 2020. Hukum Online. https://www.hukumonline.com/klinik/detail/ulasan/lt5236f79d8e4b4/mengenal-unsur-tindak-pidana-dan-syarat-pemenuhannya/. [Accessed April 25th, 2021
Montgomery, D. C., Peck, E. A., & Vining, G. G. 2012. Introduction to Linear Regression Analysis (5th ed.). Wiley Series in Probability and Statistics.
Nurdin, N., Raupong, & Islamiyati, A. 2014. Penggunaan Regresi Robust pada data yang mengandung pencilan dengan metode momen. Jurnal Matematika, Statistika Dan Komputasi, Vol 10, No.2, 115.
Perihatini, D. I. 2018. Perbandingan metode estimasi LTS , estimasi M , dan estimasi S pada Regresi Robust. Universitas Islam Indonesia.
Rini, D. S., & Faisal, F. 2015. Perbandingan Power of Test dari Uji Normalitas, metode Bayesian, Uji Shapiro-Wilk, Uji Cramer-von Mises, dan Uji Anderson-Darling. Jurnal Gradien, Vol 11, No.2, 1–5.
Safitri, D. 2015. Perbandingan metode estimasi M dan estimasi MM (Method of Moment) pada Regresi Rrobust. Universitas Islam Indonesia.
Sari, I. M., Anugrah, R., & Nasir, A. 2020. Effect of Corporate Governance and Corporate Social Responsibility on Financial Performance. Journal of Auditing, Finance, and Forensic Accounting, Vol 8, No.22, 44–54.
Seheult, A. H., Green, P. J., Rousseeuw, P. J., & Leroy, A. M. 1989. Robust regression and outlier detection. In John Wiley & Sons. John Wiley & Sons, Inc.
Sriningsih, M., Hatidja, D., & Prang, J. D. 2018. Penanganan Multikolinearitas dengan menggunakan analisis Regresi Komponen Utama pada kasus impor beras di Provinsi Sulut. Jurnal Ilmiah Sains, Vol 18, No 1, 18.
Tarno. 2007. Estimasi model Regresi Linier dengan metode Median Kuadrat Terkecil. Jurnal Sains Dan Matematika (JSM), Vol 15, No.2, 69–72.
Widodo, E., & Dewayanti, A. A. 2016. Perbandingan metode estimasi LTS, M, MM Pada Regresi robust. In Direktorat Penelitian dan Pengabdian Masyarakat UII.
Widodo, E., Suriani, E., Putri, I., & Evi, G. 2019. Analisis regresi panel pada kasus kemiskinan di Indonesia. PRISMA, Prosiding Seminar Nasional Penelitian, Vol 2, 710–717.
Zulkarnain, A., Setyo Wira, R., & Perdana, H. 2020. Analisis Regresi Robust estimasi-MM dalam mengatasi Pencilan pada Regresi Linear Berganda. Bimaster : Buletin Ilmiah Matematika, Statistika Dan Terapannya, Vol 9, No.1, 123–128.
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